Introduction: LIST Department ITIS

Here are preliminary results of the bibliometric mapping of the 2022 Luxembourg research evaluation. Its purpose is:

The method for the research-field-mapping can be reviewed here:

Rakas, M., & Hain, D. S. (2019). The state of innovation system research: What happens beneath the surface?. Research Policy, 48(9), 103787.

Seed Articles

The seed articles deemed representative for the active areas of research in the institution, and include authors affiliated with the institution. They can be selected in three ways:

  1. Via bibliographic clustering of the institutions publications and selection of most central articles per cluster (only clsuters where n >= 0.05N). Selection can be found at: https://github.com/daniel-hain/biblio_lux_2022/blob/master/output/seed/scopus_list_itis_seed.csv
  2. Manual selection of relevant publications.
  3. A combination of 1. and 2.

The present analysis is based on the following seed articles:

AU PY TI JI
WANG J;JIN C;TANG Q;LIU Z;A… 2022 CRYPTOREC: NOVEL COLLABORATIVE FILTERING RECOMMENDER MADE PRIVACY-PRESERVING EASY IEEE TRANS. DEPENDABLE SECU…
YILMA BA;PANETTO H;NAUDET Y 2021 SYSTEMIC FORMALISATION OF CYBER-PHYSICAL-SOCIAL SYSTEM (CPSS): A SYSTEMATIC LITERATURE REVIEW COMPUT IND
MAYER N;AUBERT J 2021 A RISK MANAGEMENT FRAMEWORK FOR SECURITY AND INTEGRITY OF NETWORKS AND SERVICES J. RISK RES.
GUTIERREZ-GOMEZ L;PETRY F;K… 2020 A COMPARISON FRAMEWORK OF MACHINE LEARNING ALGORITHMS FOR MIXED-TYPE VARIABLES DATASETS: A CASE S… IEEE ACCESS
MAHJRI I;FAYE S;KHADRAOUI D 2019 IMPACT AND DEPLOYMENT OF DYNAMIC TRAFFIC LIGHT CONTROL STRATEGIES USING A CITY-WIDE SIMULATION SC… IEEE INTELL. TRANSP. SYST. …
MCGEE F;GHONIEM M;MELANÇON … 2019 THE STATE OF THE ART IN MULTILAYER NETWORK VISUALIZATION COMPUT GRAPHICS FORUM
MCCALL R;MCGEE F;MIRNIG A;M… 2019 A TAXONOMY OF AUTONOMOUS VEHICLE HANDOVER SITUATIONS TRANSP. RES. PART A POLICY …
MEULEPAS JM;RONCKERS CM;SME… 2019 RADIATION EXPOSURE FROM PEDIATRIC CT SCANS AND SUBSEQUENT CANCER RISK IN THE NETHERLANDS J. NATL. CANCER INST.

Topic modelling

Here, we report the results of a LDA topic-modelling (basically, clustering on words) on all title+abstract texts.

Topics by topwords

Note: While this static vies is helpful, I recommend using the interactive LDAVis version to be found under https://daniel-hain.github.io/biblio_lux_2022/output/topic_modelling/LDAviz_list_itis.rds/index.html#topic=1&lambda=0.60&term=. For functionality and usage, see technical description in the next tab.

Topics over time

Technical Description

LDA Topic Modelling

Topic modeling is a type of statistical modeling for discovering the abstract “topics” that occur in a collection of documents. Latent Dirichlet Allocation (LDA, Blei et al., 2003) is an example of topic model and is used to classify text in a document to a particular topic.

LDA is a generative probabilistic model that assumes each topic is a mixture over an underlying set of words, and each document is a mixture of over a set of topic probabilities. It builds a topic per document model and words per topic model, modeled as Dirichlet distributions.

LDAVis

LDAvis is a web-based interactive visualisation of topics estimated using LDA (Sievert & Shirley, 2014). It provides a global view of the topics (and how they differ from each other), while at the same time allowing for a deep inspection of the terms most highly associated with each individual topic. The package extracts information from a fitted LDA topic model to inform an interactive web-based visualization. The visualisation has two basic pieces.

The left panel visualise the topics as circles in the two-dimensional plane whose centres are determined by computing the Jensen–Shannon divergence between topics, and then by using multidimensional scaling to project the inter-topic distances onto two dimensions. Each topic’s overall prevalence is encoded using the areas of the circles.

The right panel depicts a horizontal bar chart whose bars represent the individual terms that are the most useful for interpreting the currently selected topic on the left. A pair of overlaid bars represent both the corpus-wide frequency of a given term as well as the topic-specific frequency of the term.

The \(\lambda\) slider allows to rank the terms according to term relevance. By default, the terms of a topic are ranked in decreasing order according their topic-specific probability ( \(\lambda\) = 1 ). Moving the slider allows to adjust the rank of terms based on much discriminatory (or “relevant”) are for the specific topic. The suggested optimal value of \(\lambda\) is 0.6.

Knowledge Bases: Co-Citation network analysis

Note: This analysis refers the co-citation analysis, where the cited references and not the original publications are the unit of analysis. See tab Technical descriptionfor additional explanations

Knowledge Bases summary

In order to partition networks into components or clusters, we deploy a community detection technique based on the Lovain Algorithm (Blondel et al., 2008). The Lovain Algorithm is a heuristic method that attempts to optimize the modularity of communities within a network by maximizing within- and minimizing between-community connectivity. We identify the following communities = knowledge bases.

name dgr_int dgr
Knowledge Base 1: KB 1: unlabeled (n = 1073, density =7.72)
BULDYREV S.V. PARSHANI R. PAUL G. STANLEY H.E. HAVLIN S. CATASTROPHIC CASCADE OF FAILURES IN INTERDEPENDENT NETWORKS (2010) 4416 4416
KIVELÄ M. ARENAS A. BARTHELEMY M. GLEESON J.P. MORENO Y. PORTER M.A. MULTILAYER NETWORKS (2014) 2737 2740
GAO J. BULDYREV S.V. STANLEY H.E. HAVLIN S. NETWORKS FORMED FROM INTERDEPENDENT NETWORKS (2012) 2195 2198
BATTISTON F. NICOSIA V. LATORA V. STRUCTURAL MEASURES FOR MULTIPLEX NETWORKS (2014) 1502 1505
PARSHANI R. BULDYREV S.V. HAVLIN S. INTERDEPENDENT NETWORKS: REDUCING THE COUPLING STRENGTH LEADS TO A CHANGE FROM A FIRST TO SECOND ORDER PERCOLAT… 1331 1331
DE DOMENICO M. NICOSIA V. ARENAS A. LATORA V. STRUCTURAL REDUCIBILITY OF MULTILAYER NETWORKS (2015) 1324 1324
BOCCALETTI S. THE STRUCTURE AND DYNAMICS OF MULTILAYER NETWORKS (2014) 1160 1160
GAO J. BULDYREV S.V. HAVLIN S. STANLEY H.E. ROBUSTNESS OF A NETWORK OF NETWORKS (2011) 1114 1114
DE DOMENICO M. GRANELL C. PORTER M.A. ARENAS A. THE PHYSICS OF SPREADING PROCESSES IN MULTILAYER NETWORKS (2016) 1041 1041
SZELL M. LAMBIOTTE R. THURNER S. MULTIRELATIONAL ORGANIZATION OF LARGE-SCALE SOCIAL NETWORKS IN AN ONLINE WORLD (2010) 1021 1021
Knowledge Base 2: KB 2: unlabeled (n = 976, density =4.75)
PARASURAMAN R. SHERIDAN T.B. WICKENS C.D. A MODEL FOR TYPES AND LEVELS OF HUMAN INTERACTION WITH AUTOMATION (2000) 3730 3739
LEE J.D. SEE K.A. TRUST IN AUTOMATION: DESIGNING FOR APPROPRIATE RELIANCE (2004) 2118 2118
PARASURAMAN R. RILEY V. HUMANS AND AUTOMATION: USE MISUSE DISUSE ABUSE (1997) 1523 1523
ENDSLEY M.R. TOWARD A THEORY OF SITUATION AWARENESS IN DYNAMIC SYSTEMS (1995) 1221 1221
BAINBRIDGE L. IRONIES OF AUTOMATION (1983) 1008 1008
ENDSLEY M.R. KIRIS E.O. THE OUT-OF-THE-LOOP PERFORMANCE PROBLEM AND LEVEL OF CONTROL IN AUTOMATION (1995) 963 963
SHERIDAN T.B. VERPLANK W.L. (1978) 688 688
HART S.G. STAVELAND L.E. DEVELOPMENT OF NASA-TLX (TASK LOAD INDEX) 606 606
ENDSLEY M.R. SITUATION AWARENESS GLOBAL ASSESSMENT TECHNIQUE (SAGAT) 485 485
KABER D.B. ENDSLEY M.R. THE EFFECTS OF LEVEL OF AUTOMATION AND ADAPTIVE AUTOMATION ON HUMAN PERFORMANCE SITUATION AWARENESS AND WORKLOAD IN A DYNAM… 474 474
Knowledge Base 3: KB 3: unlabeled (n = 854, density =9.53)
DWORK C. MCSHERRY F. NISSIM K. SMITH A. CALIBRATING NOISE TO SENSITIVITY IN PRIVATE DATA ANALYSIS (2006) 4599 5076
DWORK C. ROTH A. THE ALGORITHMIC FOUNDATIONS OF DIFFERENTIAL PRIVACY (2014) 4381 4920
SHOKRI R. SHMATIKOV V. PRIVACY-PRESERVING DEEP LEARNING (2015) 2344 3783
DWORK C. KENTHAPADI K. MCSHERRY F. MIRONOV I. NAOR M. OUR DATA OURSELVES: PRIVACY VIA DISTRIBUTED NOISE GENERATION (2006) 1645 1744
CHAUDHURI K. MONTELEONI C. SARWATE A.D. DIFFERENTIALLY PRIVATE EMPIRICAL RISK MINIMIZATION (2011) 1484 1589
SHOKRI R. STRONATI M. SONG C. SHMATIKOV V. MEMBERSHIP INFERENCE ATTACKS AGAINST MACHINE LEARNING MODELS (2017) 1480 1934
MCSHERRY F. TALWAR K. MECHANISM DESIGN VIA DIFFERENTIAL PRIVACY (2007) 1447 1493
DWORK C. DIFFERENTIAL PRIVACY: A SURVEY OF RESULTS (2008) 1413 1736
FREDRIKSON M. JHA S. RISTENPART T. MODEL INVERSION ATTACKS THAT EXPLOIT CONFIDENCE INFORMATION AND BASIC COUNTERMEASURES (2015) 1387 1860
ABADI M. CHU A. GOODFELLOW I. MCMAHAN H.B. MIRONOV I. TALWAR K. ZHANG L. DEEP LEARNING WITH DIFFERENTIAL PRIVACY (2016) 1355 1780
Knowledge Base 4: KB 4: unlabeled (n = 737, density =10.28)
GENTRY C. FULLY HOMOMORPHIC ENCRYPTION USING IDEAL LATTICES (2009) 2152 2422
FAN J. VERCAUTEREN F. SOMEWHAT PRACTICAL FULLY HOMOMORPHIC ENCRYPTION (2012) 1888 1948
PAILLIER P. PUBLIC-KEY CRYPTOSYSTEMS BASED ON COMPOSITE DEGREE RESIDUOSITY CLASSES (1999) 1821 2149
LIU J. JUUTI M. LU Y. ASOKAN N. OBLIVIOUS NEURAL NETWORK PREDICTIONS VIA MINIONN TRANSFORMATIONS (2017) 1601 1934
MOHASSEL P. ZHANG Y. SECUREML: A SYSTEM FOR SCALABLE PRIVACY-PRESERVING MACHINE LEARNING (2017) 1517 2024
GILAD-BACHRACH R. DOWLIN N. LAINE K. LAUTER K. NAEHRIG M. WERNSING J. CRYPTONETS: APPLYING NEURAL NETWORKS TO ENCRYPTED DATA WITH HIGH THROUGHPUT A… 1205 1681
BRAKERSKI Z. GENTRY C. VAIKUNTANATHAN V. (LEVELED) 1152 1223
BRAKERSKI Z. FULLY HOMOMORPHIC ENCRYPTION WITHOUT MODULUS SWITCHING FROM CLASSICAL GAPSVP (2012) 1076 1103
CHEON J.H. KIM A. KIM M. SONG Y. HOMOMORPHIC ENCRYPTION FOR ARITHMETIC OF APPROXIMATE NUMBERS (2017) 933 954
JUVEKAR C. VAIKUNTANATHAN V. CHANDRAKASAN A. GAZELLE: A LOW LATENCY FRAMEWORK FOR SECURE NEURAL NETWORK INFERENCE (2018) 918 1048
Knowledge Base 5: KB 5: unlabeled (n = 704, density =4.52)
BREHMER M. MUNZNER T. A MULTI-LEVEL TYPOLOGY OF ABSTRACT VISUALIZATION TASKS (2013) 839 839
LEE B. PLAISANT C. PARR C.S. FEKETE J.-D. HENRY N. TASK TAXONOMY FOR GRAPH VISUALIZATION (2006) 587 587
HOLTEN D. HIERARCHICAL EDGE BUNDLES: VISUALIZATION OF ADJACENCY RELATIONS IN HIERARCHICAL DATA (2006) 524 537
SHNEIDERMAN B. THE EYES HAVE IT: A TASK BY DATA TYPE TAXONOMY FOR INFORMATION VISUALIZATIONS (1996) 499 514
GLEICHER M. ALBERS D. WALKER R. JUSUFI I. HANSEN C.D. ROBERTS J.C. VISUAL COMPARISON FOR INFORMATION VISUALIZATION (2011) 406 406
HOLTEN D. VAN WIJK J.J. FORCE-DIRECTED EDGE BUNDLING FOR GRAPH VISUALIZATION (2009) 363 363
MUNZNER T. A NESTED MODEL FOR VISUALIZATION DESIGN AND VALIDATION (2009) 353 353
MUNZNER T. (2014) 328 328
BECK F. BURCH M. DIEHL S. WEISKOPF D. THE STATE OF THE ART IN VISUALIZING DYNAMIC GRAPHS (2014) 305 308
BURCH M. VEHLOW C. BECK F. DIEHL S. WEISKOPF D. PARALLEL EDGE SPLATTING FOR SCALABLE DYNAMIC GRAPH VISUALIZATION (2011) 249 249
Knowledge Base 6: KB 6: unlabeled (n = 534, density =9.15)
BREIMAN L. RANDOM FORESTS (2001) 3748 3914
FRIEDMAN J.H. GREEDY FUNCTION APPROXIMATION: A GRADIENT BOOSTING MACHINE (2001) 2018 2070
BREIMAN L. BAGGING PREDICTORS (1996) 934 974
DEMŠAR J. STATISTICAL COMPARISONS OF CLASSIFIERS OVER MULTIPLE DATA SETS (2006) 914 920
BERGSTRA J. BENGIO Y. RANDOM SEARCH FOR HYPER-PARAMETER OPTIMIZATION (2012) 890 1068
CHEN T. GUESTRIN C. XGBOOST: A SCALABLE TREE BOOSTING SYSTEM (2016) 726 762
ALTMAN N.S. AN INTRODUCTION TO KERNEL AND NEAREST-NEIGHBOR NONPARAMETRIC REGRESSION (1992) 621 639
BROWN I. MUES C. AN EXPERIMENTAL COMPARISON OF CLASSIFICATION ALGORITHMS FOR IMBALANCED CREDIT SCORING DATA SETS (2012) 615 615
CORTES C. VAPNIK V. SUPPORT-VECTOR NETWORKS (1995) 545 549
GEURTS P. ERNST D. WEHENKEL L. EXTREMELY RANDOMIZED TREES (2006) 277 281
Knowledge Base 7: KB 7: unlabeled (n = 439, density =11.17)
PEARCE M.S. SALOTTI J.A. LITTLE M.P. RADIATION EXPOSURE FROM CT SCANS IN CHILDHOOD AND SUBSEQUENT RISK OF LEUKAEMIA AND BRAIN TUMOURS: A RETROSPECT… 1553 1553
MATHEWS J.D. FORSYTHE A.V. BRADY Z. CANCER RISK IN 680 000 PEOPLE EXPOSED TO COMPUTED TOMOGRAPHY SCANS IN CHILDHOOD OR ADOLESCENCE: DATA LINKAGE ST… 1088 1088
MIGLIORETTI D.L. JOHNSON E. WILLIAMS A. THE USE OF COMPUTED TOMOGRAPHY IN PEDIATRICS AND THE ASSOCIATED RADIATION EXPOSURE AND ESTIMATED CANCER RIS… 648 648
SIEGEL J.A. PENNINGTON C.W. SACKS B. SUBJECTING RADIOLOGIC IMAGING TO THE LINEAR NO-THRESHOLD HYPOTHESIS: A NON SEQUITUR OF NON-TRIVIAL PROPORTION … 334 334
OZASA K. SHIMIZU Y. SUYAMA A. STUDIES OF THE MORTALITY OF ATOMIC BOMB SURVIVORS REPORT 14 1950-2003: AN OVERVIEW OF CANCER AND NONCANCER DISEASES (… 333 333
SIEGEL J.A. WELSH J.S. DOES IMAGING TECHNOLOGY CAUSE CANCER? DEBUNKING THE LINEAR NO-THRESHOLD MODEL OF RADIATION CARCINOGENESIS (2016) 318 318
JOURNY N. REHEL J.L. DUCOU LE POINTE H. ARE THE STUDIES ON CANCER RISK FROM CT SCANS BIASED BY INDICATION? ELEMENTS OF ANSWER FROM A LARGE-SCALE CO… 304 304
BOUTIS K. COGOLLO W. FISCHER J. FREEDMAN S.B. BEN DAVID G. THOMAS K.E. PARENTAL KNOWLEDGE OF POTENTIAL CANCER RISKS FROM EXPOSURE TO COMPUTED TOMOG… 293 293
PEARCE M.S. SALOTTI J.A. LITTLE M.P. MCHUGH K. LEE C. KIM K.P. RADIATION EXPOSURE FROM CT SCANS IN CHILDHOOD AND SUBSEQUENT RISK OF LEUKAEMIA AND B… 259 259
CALABRESE E.J. DHAWAN G. KAPOOR R. KOZUMBO W.J. RADIOTHERAPY TREATMENT OF HUMAN INFLAMMATORY DISEASES AND CONDITIONS: OPTIMAL DOSE (2019) 250 250

Development of Knowledge Bases

Technical description

In a co-cittion network, the strength of the relationship between a reference pair \(m\) and \(n\) (\(s_{m,n}^{coc}\)) is expressed by the number of publications \(C\) which are jointly citing reference \(m\) and \(n\).

\[s_{m,n}^{coc} = \sum_i c_{i,m} c_{i,n}\]

The intuition here is that references which are frequently cited together are likely to share commonalities in theory, topic, methodology, or context. It can be interpreted as a measure of similarity as evaluated by other researchers that decide to jointly cite both references. Because the publication process is time-consuming, co-citation is a backward-looking measure, which is appropriate to map the relationship between core literature of a field.

Research Areas: Bibliographic coupling analysis

Research Areas main summary

This is arguably the more interesting part. Here, we identify the literature’s current knowledge frontier by carrying out a bibliographic coupling analysis of the publications in our corpus. This measure uses bibliographical information of publications to establish a similarity relationship between them. Again, method details to be found in the tab Technical description. As you will see, we identify the main research area, but also a set of adjacent research areas with some theoretical/methodological/application overlap.

To identify communities in the field’s knowledge frontier (labeled research areas) we again use the Lovain Algorithm (Blondel et al., 2008). We identify the following communities = research areas.

label AU PY TI dgr_int TC TC_year
Research Area 1: RA 1: unlabeled (n = 881, density =0.72)
RA 1: unlabeled CHEN T;GUESTRIN C 2016 XGBOOST: A SCALABLE TREE BOOSTING SYSTEM 3.43 11484 1914.00
RA 1: unlabeled XIA Y;LIU C;LI Y;LIU N 2017 A BOOSTED DECISION TREE APPROACH USING BAYESIAN HYPER-PARAMETER OPTIMIZATION FOR CREDIT SCORING 17.41 347 69.40
RA 1: unlabeled NAIMI B;ARAÚJO MB 2016 SDM: A REPRODUCIBLE AND EXTENSIBLE R PLATFORM FOR SPECIES DISTRIBUTION MODELLING 7.75 315 52.50
RA 1: unlabeled ZHANG C;LIU C;ZHANG X;… 2017 AN UP-TO-DATE COMPARISON OF STATE-OF-THE-ART CLASSIFICATION ALGORITHMS 8.69 220 44.00
RA 1: unlabeled XU G;WU H-Z;SHI YQ 2016 ENSEMBLE OF CNNS FOR STEGANALYSIS: AN EMPIRICAL STUDY 16.39 104 17.33
RA 1: unlabeled VRABLECOVÁ P;BOU EZZED… 2018 SMART GRID LOAD FORECASTING USING ONLINE SUPPORT VECTOR REGRESSION 18.70 80 20.00
RA 1: unlabeled BENTÉJAC C;CSÖRGŐ A;MA… 2021 A COMPARATIVE ANALYSIS OF GRADIENT BOOSTING ALGORITHMS 19.46 76 76.00
RA 1: unlabeled SHERIDAN RP;WANG WM;LI… 2016 EXTREME GRADIENT BOOSTING AS A METHOD FOR QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIPS 7.63 173 28.83
RA 1: unlabeled PROBST P;BOULESTEIX A-L 2018 TO TUNE OR NOT TO TUNE THE NUMBER OF TREES IN RANDOM FOREST 15.67 84 21.00
RA 1: unlabeled MANGALATHU S;JANG H;HW… 2020 DATA-DRIVEN MACHINE-LEARNING-BASED SEISMIC FAILURE MODE IDENTIFICATION OF REINFORCED CONCRETE SHEAR WALLS 14.30 80 40.00
Research Area 2: RA 2: unlabeled (n = 752, density =0.27)
RA 2: unlabeled ENDSLEY MR 2017 FROM HERE TO AUTONOMY: LESSONS LEARNED FROM HUMAN-AUTOMATION RESEARCH 4.92 258 51.60
RA 2: unlabeled ERIKSSON A;STANTON NA 2017 TAKEOVER TIME IN HIGHLY AUTOMATED VEHICLES: NONCRITICAL TRANSITIONS TO AND FROM MANUAL CONTROL 3.51 321 64.20
RA 2: unlabeled MERCADO JE;RUPP MA;CHE… 2016 INTELLIGENT AGENT TRANSPARENCY IN HUMAN-AGENT TEAMING FOR MULTI-UXV MANAGEMENT 4.52 147 24.50
RA 2: unlabeled PAYRE W;CESTAC J;DELHO… 2016 FULLY AUTOMATED DRIVING: IMPACT OF TRUST AND PRACTICE ON MANUAL CONTROL RECOVERY 5.12 119 19.83
RA 2: unlabeled ENDSLEY MR 2018 AUTOMATION AND SITUATION AWARENESS 2.22 260 65.00
RA 2: unlabeled SCHAEFER KE;CHEN JYC;S… 2016 A META-ANALYSIS OF FACTORS INFLUENCING THE DEVELOPMENT OF TRUST IN AUTOMATION: IMPLICATIONS FOR UNDERSTANDING AUTONOMY IN … 1.99 284 47.33
RA 2: unlabeled KABER DB 2018 ISSUES IN HUMAN–AUTOMATION INTERACTION MODELING: PRESUMPTIVE ASPECTS OF FRAMEWORKS OF TYPES AND LEVELS OF AUTOMATION 8.35 66 16.50
RA 2: unlabeled NOY IY;SHINAR D;HORREY WJ 2018 AUTOMATED DRIVING: SAFETY BLIND SPOTS 5.54 99 24.75
RA 2: unlabeled LU Z;HAPPEE R;CABRALL … 2016 HUMAN FACTORS OF TRANSITIONS IN AUTOMATED DRIVING: A GENERAL FRAMEWORK AND LITERATURE SURVEY 4.73 108 18.00
RA 2: unlabeled SHNEIDERMAN B 2020 HUMAN-CENTERED ARTIFICIAL INTELLIGENCE: RELIABLE, SAFE & TRUSTWORTHY 3.10 154 77.00
Research Area 3: RA 3: unlabeled (n = 597, density =1.26)
RA 3: unlabeled ABADI M;MCMAHAN HB;CHU… 2016 DEEP LEARNING WITH DIFFERENTIAL PRIVACY 14.91 1323 220.50
RA 3: unlabeled BONAWITZ K;IVANOV V;KR… 2017 PRACTICAL SECURE AGGREGATION FOR PRIVACY-PRESERVING MACHINE LEARNING 10.28 712 142.40
RA 3: unlabeled NASR M;SHOKRI R;HOUMAN… 2019 COMPREHENSIVE PRIVACY ANALYSIS OF DEEP LEARNING: PASSIVE AND ACTIVE WHITE-BOX INFERENCE ATTACKS AGAINST CENTRALIZED AND FE… 18.81 262 87.33
RA 3: unlabeled HITAJ B;ATENIESE G;PER… 2017 DEEP MODELS UNDER THE GAN: INFORMATION LEAKAGE FROM COLLABORATIVE DEEP LEARNING 9.92 416 83.20
RA 3: unlabeled PAPERNOT N;SONG S;MIRO… 2018 SCALABLE PRIVATE LEARNING WITH PATE 20.04 164 41.00
RA 3: unlabeled TRUEX S;STEINKE T;BARA… 2019 A HYBRID APPROACH TO PRIVACY-PRESERVING FEDERATED LEARNING 15.55 139 46.33
RA 3: unlabeled PAPERNOT N;GOODFELLOW … 2017 SEMI-SUPERVISED KNOWLEDGE TRANSFER FOR DEEP LEARNING FROM PRIVATE TRAINING DATA 21.07 99 19.80
RA 3: unlabeled JAYARAMAN B;EVANS D 2019 EVALUATING DIFFERENTIALLY PRIVATE MACHINE LEARNING IN PRACTICE 15.13 110 36.67
RA 3: unlabeled ZHU T;LI G;ZHOU W;YU PS 2017 DIFFERENTIALLY PRIVATE DATA PUBLISHING AND ANALYSIS: A SURVEY 10.87 152 30.40
RA 3: unlabeled YU L;LIU L;PU C;GURSOY… 2019 DIFFERENTIALLY PRIVATE MODEL PUBLISHING FOR DEEP LEARNING 21.22 77 25.67
Research Area 4: RA 4: unlabeled (n = 596, density =0.56)
RA 4: unlabeled DE DOMENICO M;GRANELL … 2016 THE PHYSICS OF SPREADING PROCESSES IN MULTILAYER NETWORKS 12.04 336 56.00
RA 4: unlabeled BIANCONI G 2018 MULTILAYER NETWORKS: STRUCTURE AND FUNCTION 6.34 175 43.75
RA 4: unlabeled LIU X;STANLEY HE;GAO J 2016 BREAKDOWN OF INTERDEPENDENT DIRECTED NETWORKS 10.42 89 14.83
RA 4: unlabeled RADICCHI F;BIANCONI G 2017 REDUNDANT INTERDEPENDENCIES BOOST THE ROBUSTNESS OF MULTIPLEX NETWORKS 13.22 58 11.60
RA 4: unlabeled MAJHI S;PERC M;GHOSH D 2016 CHIMERA STATES IN UNCOUPLED NEURONS INDUCED BY A MULTILAYER STRUCTURE 4.58 161 26.83
RA 4: unlabeled DEL GENIO CI;GÓMEZ-GAR… 2016 SYNCHRONIZATION IN NETWORKS WITH MULTIPLE INTERACTION LAYERS 9.22 79 13.17
RA 4: unlabeled YUAN X;HU Y;STANLEY HE… 2017 ERADICATING CATASTROPHIC COLLAPSE IN INTERDEPENDENT NETWORKS VIA REINFORCED NODES 8.65 77 15.40
RA 4: unlabeled MAJDANDZIC A;BRAUNSTEI… 2016 MULTIPLE TIPPING POINTS AND OPTIMAL REPAIRING IN INTERACTING NETWORKS 9.19 70 11.67
RA 4: unlabeled SHEKHTMAN LM;DANZIGER … 2016 RECENT ADVANCES ON FAILURE AND RECOVERY IN NETWORKS OF NETWORKS 9.13 69 11.50
RA 4: unlabeled HACKETT A;CELLAI D;GÓM… 2016 BOND PERCOLATION ON MULTIPLEX NETWORKS 10.70 58 9.67
Research Area 5: RA 5: unlabeled (n = 460, density =0.41)
RA 5: unlabeled BABL FE;BORLAND ML;PHI… 2017 ACCURACY OF PECARN, CATCH, AND CHALICE HEAD INJURY DECISION RULES IN CHILDREN: A PROSPECTIVE COHORT STUDY 8.53 147 29.40
RA 5: unlabeled PATEL AP;FISHER JL;NIC… 2019 GLOBAL, REGIONAL, AND NATIONAL BURDEN OF BRAIN AND OTHER CNS CANCER, 1990–2016: A SYSTEMATIC ANALYSIS FOR THE GLOBAL BURDE… 3.10 214 71.33
RA 5: unlabeled HONG J-Y;HAN K;JUNG J-… 2019 ASSOCIATION OF EXPOSURE TO DIAGNOSTIC LOW-DOSE IONIZING RADIATION WITH RISK OF CANCER AMONG YOUTHS IN SOUTH KOREA 7.06 64 21.33
RA 5: unlabeled MOORE MM;KULAYLAT AN;H… 2016 MAGNETIC RESONANCE IMAGING IN PEDIATRIC APPENDICITIS: A SYSTEMATIC REVIEW 6.19 57 9.50
RA 5: unlabeled MEULEPAS JM;RONCKERS C… 2019 RADIATION EXPOSURE FROM PEDIATRIC CT SCANS AND SUBSEQUENT CANCER RISK IN THE NETHERLANDS 2.74 119 39.67
RA 5: unlabeled SMITH-BINDMAN R;WANG Y… 2019 INTERNATIONAL VARIATION IN RADIATION DOSE FOR COMPUTED TOMOGRAPHY EXAMINATIONS: PROSPECTIVE COHORT STUDY 3.83 77 25.67
RA 5: unlabeled BELLOLIO MF;HEIEN HC;S… 2017 INCREASED COMPUTED TOMOGRAPHY UTILIZATION IN THE EMERGENCY DEPARTMENT AND ITS ASSOCIATION WITH HOSPITAL ADMISSION 4.57 60 12.00
RA 5: unlabeled REHANI MM;YANG K;MELIC… 2020 PATIENTS UNDERGOING RECURRENT CT SCANS: ASSESSING THE MAGNITUDE 3.41 77 38.50
RA 5: unlabeled NAGAYAMA Y;ODA S;NAKAU… 2018 RADIATION DOSE REDUCTION AT PEDIATRIC CT: USE OF LOW TUBE VOLTAGE AND ITERATIVE RECONSTRUCTION 5.38 42 10.50
RA 5: unlabeled WURZEL DF;MARCHANT JM;… 2016 PROTRACTED BACTERIAL BRONCHITIS IN CHILDREN: NATURAL HISTORY AND RISK FACTORS FOR BRONCHIECTASIS 2.97 71 11.83
Research Area 6: RA 6: unlabeled (n = 440, density =0.33)
RA 6: unlabeled TANG J;WANG K 2018 PERSONALIZED TOP-N SEQUENTIAL RECOMMENDATION VIA CONVOLUTIONAL SEQUENCE EMBEDDING 3.89 488 122.00
RA 6: unlabeled LI J;REN P;CHEN Z;REN … 2017 NEURAL ATTENTIVE SESSION-BASED RECOMMENDATION 2.93 511 102.20
RA 6: unlabeled YU F;LIU Q;WU S;WANG L… 2016 A DYNAMIC RECURRENT MODEL FOR NEXT BASKET RECOMMENDATION 2.75 256 42.67
RA 6: unlabeled LIN K;ZHAO R;XU Z;ZHOU J 2018 EFFICIENT LARGE-SCALE FLEET MANAGEMENT VIA MULTI-AGENT DEEP REINFORCEMENT LEARNING 3.48 135 33.75
RA 6: unlabeled MAN T;SHEN H;JIN X;CHE… 2017 CROSS-DOMAIN RECOMMENDATION: AN EMBEDDING AND MAPPING APPROACH 4.03 116 23.20
RA 6: unlabeled YU X;JIANG F;DU J;GONG D 2019 A CROSS-DOMAIN COLLABORATIVE FILTERING ALGORITHM WITH EXPANDING USER AND ITEM FEATURES VIA THE LATENT FACTOR SPACE OF AUXI… 2.73 117 39.00
RA 6: unlabeled CHAE D-K;KIM S-W;KANG … 2018 CFGAN: A GENERIC COLLABORATIVE FILTERING FRAMEWORK BASED ON GENERATIVE ADVERSARIAL NETWORKS 2.59 98 24.50
RA 6: unlabeled ZHENG Y;TANG B;DING W;… 2016 A NEURAL AUTOREGRESSIVE APPROACH TO COLLABORATIVE FILTERING 3.38 73 12.17
RA 6: unlabeled TORRADO RR;BONTRAGER P… 2018 DEEP REINFORCEMENT LEARNING FOR GENERAL VIDEO GAME AI 4.20 51 12.75
RA 6: unlabeled LI P;TUZHILIN A 2020 DDTCDR: DEEP DUAL TRANSFER CROSS DOMAIN RECOMMENDATION 4.62 45 22.50
Research Area 7: RA 7: unlabeled (n = 376, density =0.27)
RA 7: unlabeled SATYANARAYAN A;MORITZ … 2017 VEGA-LITE: A GRAMMAR OF INTERACTIVE GRAPHICS 1.07 315 63.00
RA 7: unlabeled WONGSUPHASAWAT K;MORIT… 2016 VOYAGER: EXPLORATORY ANALYSIS VIA FACETED BROWSING OF VISUALIZATION RECOMMENDATIONS 1.29 236 39.33
RA 7: unlabeled BECK F;BURCH M;DIEHL S… 2017 A TAXONOMY AND SURVEY OF DYNAMIC GRAPH VISUALIZATION 1.73 162 32.40
RA 7: unlabeled GLEICHER M 2018 CONSIDERATIONS FOR VISUALIZING COMPARISON 3.20 84 21.00
RA 7: unlabeled VON LANDESBERGER T;BRO… 2016 MOBILITYGRAPHS: VISUAL ANALYSIS OF MASS MOBILITY DYNAMICS VIA SPATIO-TEMPORAL GRAPHS AND CLUSTERING 1.69 142 23.67
RA 7: unlabeled LAM H;TORY M;MUNZNER T 2018 BRIDGING FROM GOALS TO TASKS WITH DESIGN STUDY ANALYSIS REPORTS 3.20 33 8.25
RA 7: unlabeled WU Y;PITIPORNVIVAT N;Z… 2016 EGOSLIDER: VISUAL ANALYSIS OF EGOCENTRIC NETWORK EVOLUTION 1.41 72 12.00
RA 7: unlabeled SRINIVASAN A;STASKO J 2018 ORKO: FACILITATING MULTIMODAL INTERACTION FOR VISUAL EXPLORATION AND ANALYSIS OF NETWORKS 1.64 62 15.50
RA 7: unlabeled ANDRIENKO G;ANDRIENKO … 2017 REVEALING PATTERNS AND TRENDS OF MASS MOBILITY THROUGH SPATIAL AND TEMPORAL ABSTRACTION OF ORIGIN-DESTINATION MOVEMENT DATA 1.46 68 13.60
RA 7: unlabeled LIU D;WENG D;LI Y;BAO … 2017 SMARTADP: VISUAL ANALYTICS OF LARGE-SCALE TAXI TRAJECTORIES FOR SELECTING BILLBOARD LOCATIONS 0.82 117 23.40
Research Area 8: RA 8: unlabeled (n = 373, density =1.15)
RA 8: unlabeled YANG Q;LIU Y;CHEN T;TO… 2019 FEDERATED MACHINE LEARNING: CONCEPT AND APPLICATIONS 4.02 1279 426.33
RA 8: unlabeled MOHASSEL P;ZHANG Y 2017 SECUREML: A SYSTEM FOR SCALABLE PRIVACY-PRESERVING MACHINE LEARNING 4.37 589 117.80
RA 8: unlabeled JUVEKAR C;VAIKUNTANATH… 2018 GAZELLE: A LOW LATENCY FRAMEWORK FOR SECURE NEURAL NETWORK INFERENCE 10.61 222 55.50
RA 8: unlabeled LIU J;JUUTI M;LU Y;ASO… 2017 OBLIVIOUS NEURAL NETWORK PREDICTIONS VIA MINIONN TRANSFORMATIONS 4.93 240 48.00
RA 8: unlabeled JIANG X;LAUTER K;KIM M… 2018 SECURE OUTSOURCED MATRIX COMPUTATION AND APPLICATION TO NEURAL NETWORKS 12.70 82 20.50
RA 8: unlabeled CHEON JH;HAN K;KIM A;K… 2018 BOOTSTRAPPING FOR APPROXIMATE HOMOMORPHIC ENCRYPTION 11.83 74 18.50
RA 8: unlabeled SADEGH RIAZI M;SONGHOR… 2018 CHAMELEON: A HYBRID SECURE COMPUTATION FRAMEWORK FOR MACHINE LEARNING APPLICATIONS 6.14 132 33.00
RA 8: unlabeled MOHASSEL P;RINDAL P 2018 ABY3: A MIXED PROTOCOL FRAMEWORK FOR MACHINE LEARNING 3.77 199 49.75
RA 8: unlabeled SADEGH RIAZI M;SAMRAGH… 2019 XONN: XNOR-BASED OBLIVIOUS DEEP NEURAL NETWORK INFERENCE 9.99 75 25.00
RA 8: unlabeled DOWLIN N;GILAD-BACHRAC… 2017 MANUAL FOR USING HOMOMORPHIC ENCRYPTION FOR BIOINFORMATICS: THIS PAPER PROVIDES A NEW HOMOMORPHIC ENCRYPTION ALGORITHM AND… 10.41 52 10.40

Development

Connectivity between the research areas

Technical description

In a bibliographic coupling network, the coupling-strength between publications is determined by the number of commonly cited references they share, assuming a common pool of references to indicate similarity in context, methods, or theory. Formally, the strength of the relationship between a publication pair \(i\) and \(j\) (\(s_{i,j}^{bib}\)) is expressed by the number of commonly cited references.

\[s_{i,j}^{bib} = \sum_m c_{i,m} c_{j,m}\]

Since our corpus contains publications which differ strongly in terms of the number of cited references, we normalize the coupling strength by the Jaccard similarity coefficient. Here, we weight the intercept of two publications’ bibliography (shared refeences) by their union (number of all references cited by either \(i\) or \(j\)). It is bounded between zero and one, where one indicates the two publications to have an identical bibliography, and zero that they do not share any cited reference. Thereby, we prevent publications from having high coupling strength due to a large bibliography (e.g., literature surveys).

\[S_{i,j}^{jac-bib} =\frac{C(i \cap j)}{C(i \cup j)} = \frac{s_{i,j}^{bib}}{c_i + c_j - s_{i,j}^{bib}}\]

More recent articles have a higher pool of possible references to co-cite to, hence they are more likely to be coupled. Consequently, bibliographic coupling represents a forward looking measure, and the method of choice to identify the current knowledge frontier at the point of analysis.

Knowledge Bases, Research Areas & Topics Interaction

Endnotes

All results are preliminary so far…